# [R] How to Extract Information from SIMEX Output

Tom La Bone booboo at gforcecable.com
Tue Apr 19 02:52:57 CEST 2011

```Below is a SIMEX object that was generated with the "simex" function from the
"simex" package applied to a logistic regression fit. From this mountain of
information I would like to extract all of the values summarized in this
line:

.. ..\$ variance.jackknife: num [1:5, 1:4] 1.684 1.144 0.85 0.624 0.519 ...

Can someone suggest how to go about doing this? I can extract the upper
level results like fit.simex\$coefficients but I have had no success getting
at the lower levels.

Tom

> str(fit.simex)
List of 24
\$ coefficients             : Named num [1:2] -17.1 3
..- attr(*, "names")= chr [1:2] "(Intercept)" "x"
\$ SIMEX.estimates          : num [1:6, 1:3] -1 0 0.5 1 1.5 ...
..- attr(*, "dimnames")=List of 2
.. ..\$ : NULL
.. ..\$ : chr [1:3] "lambda" "(Intercept)" "x"
\$ lambda                   : num [1:5] 0 0.5 1 1.5 2
\$ model                    :List of 31
..\$ coefficients     : Named num [1:2] -13.27 2.32
.. ..- attr(*, "names")= chr [1:2] "(Intercept)" "x"
..\$ residuals        : Named num [1:1615] -1.12 -1.42 -1.23 -1.07 -1.44
...
.. ..- attr(*, "names")= chr [1:1615] "1" "2" "3" "4" ...
..\$ fitted.values    : Named num [1:1615] 0.1032 0.2952 0.1847 0.0656
0.3062 ...
.. ..- attr(*, "names")= chr [1:1615] "1" "2" "3" "4" ...
..\$ effects          : Named num [1:1615] 19.552 -9.275 -0.473 -0.283
-0.641 ...
.. ..- attr(*, "names")= chr [1:1615] "(Intercept)" "x" "" "" ...
..\$ R                : num [1:2, 1:2] -15.6 0 -81 -4
.. ..- attr(*, "dimnames")=List of 2
.. .. ..\$ : chr [1:2] "(Intercept)" "x"
.. .. ..\$ : chr [1:2] "(Intercept)" "x"
..\$ rank             : int 2
..\$ qr               :List of 5
.. ..\$ qr   : num [1:1615, 1:2] -15.6232 0.0292 0.0248 0.0159 0.0295 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..\$ : chr [1:1615] "1" "2" "3" "4" ...
.. .. .. ..\$ : chr [1:2] "(Intercept)" "x"
.. ..\$ rank : int 2
.. ..\$ qraux: num [1:2] 1.02 1.02
.. ..\$ pivot: int [1:2] 1 2
.. ..\$ tol  : num 1e-11
.. ..- attr(*, "class")= chr "qr"
..\$ family           :List of 12
.. ..\$ family    : chr "binomial"
.. ..\$ link      : chr "logit"
.. ..\$ variance  :function (mu)
.. ..\$ dev.resids:function (y, mu, wt)
.. ..\$ aic       :function (y, n, mu, wt, dev)
.. ..\$ mu.eta    :function (eta)
.. ..\$ initialize:  expression({     if (NCOL(y) == 1) {         if
(is.factor(y))              y <- y != levels(y)[1L]         n <- rep.int(1,
nobs)         y[weights == 0] <- 0         if (any(y < 0 | y > 1))
stop("y values must be 0 <= y <= 1")         mustart <- (weights * y +
0.5)/(weights + 1)         m <- weights * y         if (any(abs(m -
round(m)) > 0.001))              warning("non-integer #successes in a
binomial glm!")     }     else if (NCOL(y) == 2) {         if (any(abs(y -
round(y)) > 0.001))              warning("non-integer counts in a binomial
glm!")         n <- y[, 1] + y[, 2]         y <- ifelse(n == 0, 0, y[, 1]/n)
weights <- weights * n         mustart <- (n * y + 0.5)/(n + 1)     }
else stop("for the binomial family, y must be a vector of 0 and 1's\n",
"or a 2 column matrix where col 1 is no. successes and col 2 is no.
failures") })
.. ..\$ validmu   :function (mu)
.. ..\$ valideta  :function (eta)
.. ..\$ simulate  :function (object, nsim)
.. ..- attr(*, "class")= chr "family"
..\$ linear.predictors: Named num [1:1615] -2.162 -0.87 -1.485 -2.656
-0.818 ...
.. ..- attr(*, "names")= chr [1:1615] "1" "2" "3" "4" ...
..\$ deviance         : num 1521
..\$ aic              : num 1525
..\$ null.deviance    : num 1622
..\$ iter             : int 4
..\$ weights          : Named num [1:1615] 0.0926 0.208 0.1506 0.0613
0.2125 ...
.. ..- attr(*, "names")= chr [1:1615] "1" "2" "3" "4" ...
..\$ prior.weights    : Named num [1:1615] 1 1 1 1 1 1 1 1 1 1 ...
.. ..- attr(*, "names")= chr [1:1615] "1" "2" "3" "4" ...
..\$ df.residual      : int 1613
..\$ df.null          : int 1614
..\$ y                : Named num [1:1615] 0 0 0 0 0 0 0 0 0 0 ...
.. ..- attr(*, "names")= chr [1:1615] "1" "2" "3" "4" ...
..\$ converged        : logi TRUE
..\$ boundary         : logi FALSE
..\$ model            :'data.frame':	1615 obs. of  2 variables:
.. ..\$ y: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
.. ..\$ x: num [1:1615] 4.79 5.35 5.09 4.58 5.37 ...
.. ..- attr(*, "terms")=Classes 'terms', 'formula' length 3 y ~ x
.. .. .. ..- attr(*, "variables")= language list(y, x)
.. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
.. .. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. .. ..\$ : chr [1:2] "y" "x"
.. .. .. .. .. ..\$ : chr "x"
.. .. .. ..- attr(*, "term.labels")= chr "x"
.. .. .. ..- attr(*, "order")= int 1
.. .. .. ..- attr(*, "intercept")= int 1
.. .. .. ..- attr(*, "response")= int 1
.. .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
.. .. .. ..- attr(*, "predvars")= language list(y, x)
.. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "factor" "numeric"
.. .. .. .. ..- attr(*, "names")= chr [1:2] "y" "x"
..\$ x                : num [1:1615, 1:2] 1 1 1 1 1 1 1 1 1 1 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..\$ : chr [1:1615] "1" "2" "3" "4" ...
.. .. ..\$ : chr [1:2] "(Intercept)" "x"
.. ..- attr(*, "assign")= int [1:2] 0 1
..\$ call             : language glm(formula = y ~ x, family = binomial, x
= TRUE, y = TRUE)
..\$ formula          :Class 'formula' length 3 y ~ x
.. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
..\$ terms            :Classes 'terms', 'formula' length 3 y ~ x
.. .. ..- attr(*, "variables")= language list(y, x)
.. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
.. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. ..\$ : chr [1:2] "y" "x"
.. .. .. .. ..\$ : chr "x"
.. .. ..- attr(*, "term.labels")= chr "x"
.. .. ..- attr(*, "order")= int 1
.. .. ..- attr(*, "intercept")= int 1
.. .. ..- attr(*, "response")= int 1
.. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
.. .. ..- attr(*, "predvars")= language list(y, x)
.. .. ..- attr(*, "dataClasses")= Named chr [1:2] "factor" "numeric"
.. .. .. ..- attr(*, "names")= chr [1:2] "y" "x"
..\$ data             :<environment: R_GlobalEnv>
..\$ offset           : NULL
..\$ control          :List of 3
.. ..\$ epsilon: num 1e-08
.. ..\$ maxit  : num 25
.. ..\$ trace  : logi FALSE
..\$ method           : chr "glm.fit"
..\$ contrasts        : NULL
..\$ xlevels          : Named list()
..- attr(*, "class")= chr [1:2] "glm" "lm"
\$ mc.matrix                :List of 1
..\$ y: num [1:2, 1:2] 0.95 0.05 0.03 0.97
.. ..- attr(*, "dimnames")=List of 2
.. .. ..\$ : chr [1:2] "0" "1"
.. .. ..\$ : chr [1:2] "0" "1"
\$ B                        : num 800
\$ extrapolation            :List of 12
..\$ coefficients : num [1:3, 1:2] -13.258 3.299 -0.501 2.315 -0.593 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..\$ : chr [1:3] "(Intercept)" "lambda" "I(lambda^2)"
.. .. ..\$ : chr [1:2] "(Intercept)" "x"
..\$ residuals    : num [1:5, 1:2] -0.01292 0.02805 -0.00663 -0.01922
0.01071 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..\$ : chr [1:5] "1" "2" "3" "4" ...
.. .. ..\$ : chr [1:2] "(Intercept)" "x"
..\$ effects      : num [1:5, 1:2] 23.9505 3.63099 -0.46906 -0.00164
0.03846 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..\$ : chr [1:5] "(Intercept)" "lambda" "I(lambda^2)" "" ...
.. .. ..\$ : chr [1:2] "(Intercept)" "x"
..\$ rank         : int 3
..\$ fitted.values: num [1:5, 1:2] -13.26 -11.73 -10.46 -9.44 -8.67 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..\$ : chr [1:5] "1" "2" "3" "4" ...
.. .. ..\$ : chr [1:2] "(Intercept)" "x"
..\$ assign       : int [1:3] 0 1 2
..\$ qr           :List of 5
.. ..\$ qr   : num [1:5, 1:3] -2.236 0.447 0.447 0.447 0.447 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..\$ : chr [1:5] "1" "2" "3" "4" ...
.. .. .. ..\$ : chr [1:3] "(Intercept)" "lambda" "I(lambda^2)"
.. .. ..- attr(*, "assign")= int [1:3] 0 1 2
.. ..\$ qraux: num [1:3] 1.45 1.12 1.78
.. ..\$ pivot: int [1:3] 1 2 3
.. ..\$ tol  : num 1e-07
.. ..\$ rank : int 3
.. ..- attr(*, "class")= chr "qr"
..\$ df.residual  : int 2
..\$ xlevels      : Named list()
..\$ call         : language lm(formula = estimates ~ lambda + I(lambda^2))
..\$ terms        :Classes 'terms', 'formula' length 3 estimates ~ lambda +
I(lambda^2)
.. .. ..- attr(*, "variables")= language list(estimates, lambda,
I(lambda^2))
.. .. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1
.. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. ..\$ : chr [1:3] "estimates" "lambda" "I(lambda^2)"
.. .. .. .. ..\$ : chr [1:2] "lambda" "I(lambda^2)"
.. .. ..- attr(*, "term.labels")= chr [1:2] "lambda" "I(lambda^2)"
.. .. ..- attr(*, "order")= int [1:2] 1 1
.. .. ..- attr(*, "intercept")= int 1
.. .. ..- attr(*, "response")= int 1
.. .. ..- attr(*, ".Environment")=<environment: 0x0bd89124>
.. .. ..- attr(*, "predvars")= language list(estimates, lambda,
I(lambda^2))
.. .. ..- attr(*, "dataClasses")= Named chr [1:3] "nmatrix.2" "numeric"
"numeric"
.. .. .. ..- attr(*, "names")= chr [1:3] "estimates" "lambda"
"I(lambda^2)"
..\$ model        :'data.frame':	5 obs. of  3 variables:
.. ..\$ estimates  : num [1:5, 1:2] -13.27 -11.71 -10.47 -9.46 -8.65 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..\$ : NULL
.. .. .. ..\$ : chr [1:2] "(Intercept)" "x"
.. ..\$ lambda     : num [1:5] 0 0.5 1 1.5 2
.. ..\$ I(lambda^2):Class 'AsIs'  num [1:5] 0 0.25 1 2.25 4
.. ..- attr(*, "terms")=Classes 'terms', 'formula' length 3 estimates ~
lambda + I(lambda^2)
.. .. .. ..- attr(*, "variables")= language list(estimates, lambda,
I(lambda^2))
.. .. .. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1
.. .. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. .. ..\$ : chr [1:3] "estimates" "lambda" "I(lambda^2)"
.. .. .. .. .. ..\$ : chr [1:2] "lambda" "I(lambda^2)"
.. .. .. ..- attr(*, "term.labels")= chr [1:2] "lambda" "I(lambda^2)"
.. .. .. ..- attr(*, "order")= int [1:2] 1 1
.. .. .. ..- attr(*, "intercept")= int 1
.. .. .. ..- attr(*, "response")= int 1
.. .. .. ..- attr(*, ".Environment")=<environment: 0x0bd89124>
.. .. .. ..- attr(*, "predvars")= language list(estimates, lambda,
I(lambda^2))
.. .. .. ..- attr(*, "dataClasses")= Named chr [1:3] "nmatrix.2" "numeric"
"numeric"
.. .. .. .. ..- attr(*, "names")= chr [1:3] "estimates" "lambda"
"I(lambda^2)"
..- attr(*, "class")= chr [1:2] "mlm" "lm"
\$ SIMEXvariable            : chr "y"
\$ call                     : language mcsimex(model = fit.naive,
SIMEXvariable = "y", mc.matrix = P,      lambda = c(0.5, 1, 1.5, 2), B =
\$ theta                    :List of 2
..\$ (Intercept):'data.frame':	800 obs. of  4 variables:
.. ..\$ X1: num [1:800] -11.9 -12.4 -11.1 -11.4 -11 ...
.. ..\$ X2: num [1:800] -11.8 -10.05 -9.78 -10.97 -11.12 ...
.. ..\$ X3: num [1:800] -9 -8.71 -9.88 -8.73 -8.65 ...
.. ..\$ X4: num [1:800] -8.49 -8.7 -9.33 -8.23 -8.89 ...
..\$ x          :'data.frame':	800 obs. of  4 variables:
.. ..\$ X1: num [1:800] 2.07 2.16 1.93 1.97 1.9 ...
.. ..\$ X2: num [1:800] 2.07 1.73 1.69 1.9 1.94 ...
.. ..\$ X3: num [1:800] 1.56 1.49 1.72 1.49 1.48 ...
.. ..\$ X4: num [1:800] 1.47 1.51 1.63 1.42 1.54 ...
\$ fitted.values            : Named num [1:1615] 0.0645 0.2685 0.1421 0.0351
0.2821 ...
..- attr(*, "names")= chr [1:1615] "1" "2" "3" "4" ...
\$ residuals                : Named num [1:1615] -0.0645 -0.2685 -0.1421
-0.0351 -0.2821 ...
..- attr(*, "names")= chr [1:1615] "1" "2" "3" "4" ...
\$ extrapolation.variance   :List of 12
..\$ coefficients : num [1:3, 1:4] 1.668 -1.105 0.268 -0.321 0.209 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..\$ : chr [1:3] "(Intercept)" "lambda" "I(lambda^2)"
.. .. ..\$ : NULL
..\$ residuals    : num [1:5, 1:4] 0.0159 -0.0385 0.0199 0.012 -0.0093 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..\$ : chr [1:5] "1" "2" "3" "4" ...
.. .. ..\$ : NULL
..\$ effects      : num [1:5, 1:4] -2.1556 -0.9011 0.2503 -0.0154 -0.0461
...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..\$ : chr [1:5] "(Intercept)" "lambda" "I(lambda^2)" "" ...
.. .. ..\$ : NULL
..\$ rank         : int 3
..\$ fitted.values: num [1:5, 1:4] 1.668 1.182 0.83 0.612 0.528 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..\$ : chr [1:5] "1" "2" "3" "4" ...
.. .. ..\$ : NULL
..\$ assign       : int [1:3] 0 1 2
..\$ qr           :List of 5
.. ..\$ qr   : num [1:5, 1:3] -2.236 0.447 0.447 0.447 0.447 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..\$ : chr [1:5] "1" "2" "3" "4" ...
.. .. .. ..\$ : chr [1:3] "(Intercept)" "lambda" "I(lambda^2)"
.. .. ..- attr(*, "assign")= int [1:3] 0 1 2
.. ..\$ qraux: num [1:3] 1.45 1.12 1.78
.. ..\$ pivot: int [1:3] 1 2 3
.. ..\$ tol  : num 1e-07
.. ..\$ rank : int 3
.. ..- attr(*, "class")= chr "qr"
..\$ df.residual  : int 2
..\$ xlevels      : Named list()
..\$ call         : language lm(formula = variance.jackknife ~ lambda +
I(lambda^2))
..\$ terms        :Classes 'terms', 'formula' length 3 variance.jackknife ~
lambda + I(lambda^2)
.. .. ..- attr(*, "variables")= language list(variance.jackknife, lambda,
I(lambda^2))
.. .. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1
.. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. ..\$ : chr [1:3] "variance.jackknife" "lambda" "I(lambda^2)"
.. .. .. .. ..\$ : chr [1:2] "lambda" "I(lambda^2)"
.. .. ..- attr(*, "term.labels")= chr [1:2] "lambda" "I(lambda^2)"
.. .. ..- attr(*, "order")= int [1:2] 1 1
.. .. ..- attr(*, "intercept")= int 1
.. .. ..- attr(*, "response")= int 1
.. .. ..- attr(*, ".Environment")=<environment: 0x0bd89124>
.. .. ..- attr(*, "predvars")= language list(variance.jackknife, lambda,
I(lambda^2))
.. .. ..- attr(*, "dataClasses")= Named chr [1:3] "nmatrix.4" "numeric"
"numeric"
.. .. .. ..- attr(*, "names")= chr [1:3] "variance.jackknife" "lambda"
"I(lambda^2)"
..\$ model        :'data.frame':	5 obs. of  3 variables:
.. ..\$ variance.jackknife: num [1:5, 1:4] 1.684 1.144 0.85 0.624 0.519 ...
.. ..\$ lambda            : num [1:5] 0 0.5 1 1.5 2
.. ..\$ I(lambda^2)       :Class 'AsIs'  num [1:5] 0 0.25 1 2.25 4
.. ..- attr(*, "terms")=Classes 'terms', 'formula' length 3
variance.jackknife ~ lambda + I(lambda^2)
.. .. .. ..- attr(*, "variables")= language list(variance.jackknife,
lambda, I(lambda^2))
.. .. .. ..- attr(*, "factors")= int [1:3, 1:2] 0 1 0 0 0 1
.. .. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. .. ..\$ : chr [1:3] "variance.jackknife" "lambda" "I(lambda^2)"
.. .. .. .. .. ..\$ : chr [1:2] "lambda" "I(lambda^2)"
.. .. .. ..- attr(*, "term.labels")= chr [1:2] "lambda" "I(lambda^2)"
.. .. .. ..- attr(*, "order")= int [1:2] 1 1
.. .. .. ..- attr(*, "intercept")= int 1
.. .. .. ..- attr(*, "response")= int 1
.. .. .. ..- attr(*, ".Environment")=<environment: 0x0bd89124>
.. .. .. ..- attr(*, "predvars")= language list(variance.jackknife,
lambda, I(lambda^2))
.. .. .. ..- attr(*, "dataClasses")= Named chr [1:3] "nmatrix.4" "numeric"
"numeric"
.. .. .. .. ..- attr(*, "names")= chr [1:3] "variance.jackknife" "lambda"
"I(lambda^2)"
..- attr(*, "class")= chr [1:2] "mlm" "lm"
\$ variance.jackknife       : num [1:2, 1:2] 3.04 -0.58 -0.58 0.111
..- attr(*, "dimnames")=List of 2
.. ..\$ : chr [1:2] "(Intercept)" "x"
.. ..\$ : chr [1:2] "(Intercept)" "x"
\$ variance.jackknife.lambda: num [1:6, 1:5] -1 0 0.5 1 1.5 ...
..- attr(*, "dimnames")=List of 2
.. ..\$ : chr [1:6] "1" "" "" "" ...
.. ..\$ : NULL
\$ PSI                      : num [1:1615, 1:10] -0.1032 -0.2952 -0.1847
-0.0656 -0.3062 ...
..- attr(*, "dimnames")=List of 2
.. ..\$ : chr [1:1615] "1" "2" "3" "4" ...
.. ..\$ : chr [1:10] "(Intercept)" "x" "(Intercept)" "x" ...
\$ c11                      : num [1:10, 1:10] 0.151 0.78 0.144 0.748 0.139
...
..- attr(*, "dimnames")=List of 2
.. ..\$ : chr [1:10] "(Intercept)" "x" "(Intercept)" "x" ...
.. ..\$ : chr [1:10] "(Intercept)" "x" "(Intercept)" "x" ...
\$ a11                      : num [1:10, 1:10] -0.151 -0.784 0 0 0 ...
\$ sigma                    : num [1:10, 1:10] 2979 -572 2482 -478 2132 ...
\$ sigma.gamma              : num [1:6, 1:6] 2961 -1030 192 -569 195 ...
\$ g                        : num [1:6, 1:2] 1 -1 1 0 0 0 0 0 0 1 ...
\$ s                        : num [1:6, 1:10] -1 0 0 0 0 0 0 0 0 -1 ...
\$ variance.asymptotic      : num [1:2, 1:2] 3.697 -0.704 -0.704 0.134
..- attr(*, "dimnames")=List of 2
.. ..\$ : chr [1:2] "(Intercept)" "x"
.. ..\$ : chr [1:2] "(Intercept)" "x"
- attr(*, "class")= chr "mcsimex"

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